Zeerak Talat, an independent scholar, gave a talk at MBZUAI on ethical concerns in NLP. The talk covered disparities in research on biases in NLP, performance differences based on socio-economic language variations, and risks of malicious reuse of NLP tools. Talat's research considers how machine learning interacts with and impacts societies through content moderation technologies. Why it matters: As NLP technologies become more integrated into society, understanding and addressing their potential harms and ethical implications is crucial for responsible development and deployment in the region and beyond.
The Russian Immune Diversity Atlas project aims to profile immune cells from people of different ancestries at a multiomics level. The goal is to reconstruct a reference atlas of the healthy immune system and investigate its perturbations in Type II Diabetes (T2D). The project seeks to identify novel mechanisms and genetic/epigenetic markers for early T2D diagnostics, prognosis, and therapy as part of the international Human Cell Atlas. Why it matters: Addressing genetic diversity in biomedical research, particularly in the context of the Human Cell Atlas, is crucial for personalized medicine and ensuring that treatments are effective across diverse populations in the Middle East and globally.
MBZUAI researchers release JEEM, a new benchmark dataset for evaluating vision-language models on Arabic dialects. The dataset covers image captioning and visual question answering tasks using images from Jordan, UAE, Egypt, and Morocco. Results show models struggle with cultural understanding and relevance despite fluent language generation.
This study introduces a Probabilistic Graphical Model (PGM) framework utilizing Pearl's do-operator to causally audit LLM safety mechanisms, specifically isolating the effect of injecting cultural demographics into prompts. A large-scale empirical analysis was conducted across seven instruction-tuned models from diverse origins, including the UAE's Falcon3-7B, as well as models from the US, Europe, China, and India, using ToxiGen and BOLD datasets. The findings revealed a disparity between observational and interventional bias, demonstrating that standard fairness metrics can overestimate demographic bias. Western models exhibited higher causal refusal rates for specific demographic groups, while Eastern models showed low overall intervention rates with targeted sensitivities toward regional demographics. Why it matters: This research highlights the geopolitical nuances of LLM safety alignment and the potential for demographic-sensitive over-triggering to restrict benign discourse, which is particularly relevant for diverse regions like the Middle East in developing culturally-aware AI.